Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well

Surface and Sub-Surface Analyses for Bridge Inspection

The development of bridge inspection solutions has been discussed in the recent past. In this dissertation, significant development and improvement on the state-of-the-art in the field of bridge inspection using multiple sensors (e.g. ground penetrating radar (GPR) and visual sensor) has been proposed. In the first part of this research (discussed in chapter 3), the focus is towards developing effective and novel methods for rebar detection and localization for sub-surface bridge inspection of steel rebars. The data has been collected using Ground Penetrating Radar (GPR) sensor on real bridge decks. In this regard, a number of different approaches have been successively developed that continue to improve the state-of-the-art in this particular research area. The second part (discussed in chapter 4) of this research deals with the development of an automated system for steel bridge defect detection system using a Multi-Directional Bicycle Robot. The training data has been acquired from actual bridges in Vietnam and validation is performed on data collected using Bicycle Robot from actual bridge located in Highway-80, Lovelock, Nevada, USA. A number of different proposed methods have been discussed in chapter 4. The final chapter of the dissertation will conclude the findings from the different parts and discuss ways of improving on the existing works in the near future

Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network

The article discusses how impact actions, such as conflict and warfare, can negatively impact the structural integrity of concrete structures and how detecting hidden defects in concrete structures is difficult without expert knowledge. The paper presents a new technique that combines thermal imaging and artificial intelligence to detect hidden defects in concrete structures. The authors trained an AI model on simulated data and achieved a validation accuracy of 99.93%. They then conducted a laboratory experiment to create a dataset of concrete blocks with and without subsurface cracks and trained a new model, which achieved a validation accuracy of 100%. The article concludes that AI can detect hidden defects and subsurface cracks in concrete structures by classifying thermal images of concrete surfaces

Transient thermography for flaw detection in friction stir welding : a machine learning approach

A systematic computational method to simulate and detect sub-surface flaws, through non-destructive transient thermography, in aluminium sheets and friction stir welded sheets is proposed. The proposed method relies on feature extraction methods and a data driven machine learning modelling structure. In this work, we propose the use of a multi-layer perceptron feed-forward neural-network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose Thermographic Signal Linear Modelling (TSLM), a hyper-parameterfree feature extraction technique for transient thermography. The new feature extraction and modelling framework was tested with out-of-sample experimental transient thermography data and results show effectiveness in sub-surface flaw detection of up to 2.3 mm deep in aluminium sheets (99.8 % true positive rate, 92.1 % true negative rate) and up to 2.2 mm deep in friction stir welds (97.2 % true positive rate, 87.8 % true negative rate)

Evaluation of new technologies to support asset management of metro systems

Since 1930, London Underground Limited (LUL) has performed visual inspections to understand the condition of the physical assets such as tunnels, bridges and structures. The major problem with this kind of inspection is the lack in quality of the data, as it depends on the ability of the inspector to assess and interpret the condition of the asset both accurately and with repeatability. In addition, data collection is time-consuming and, therefore, costly when the whole of the metro network needs to be regularly inspected and there are limited periods when access is available. The problems associated with access to the infrastructure have increased significantly with the implementation of the night tube and will increase further as the night tube is extended over the next 5 to 10 years. To determine the condition of metro assets and to predict the need for intervention, monitoring the changes in the assets’ condition is key to any further evaluation and maintenance planning. This thesis presents the outcomes of using new technologies such as Thermography, Kinematic and Static Laser Scanning, Close-Range Photogrammetry and Total Station to measure defects, such as water seepage, mortar loss in joints, lining face loss (in brick tunnels), cracks, corrosion, voids, cavities and spalls. Each technique is explored through three case studies that evaluate the performance and limitation in the determination of the asset condition. The first case study was performed to compare and contrast the use of Euroconsult’s high definition laser survey against a Principal Inspection Report to determine the level of consistency in predicting the asset condition. During this case study, reports from laser surveys and principal inspections of brick tunnels and covered ways were compared. This analysis showed that a direct comparison between the two inspections is not appropriate because the laser inspection does not capture all the defects mentioned in the Engineering Standard S1060. It also showed that to close the gap between the laser survey and visual inspection, laser surveys would have to be performed every year in brick tunnels and then compare any changes in asset condition with that from the previous scan. The second case study was performed using Infrared Thermography (IRT) to identify water seepage in the brick tunnels as well as test the system in a configuration that would allow the survey to be done from an engineering train. A set of calibration tests were performed in the lab and later the technique was trialled on an engineering train. The results showed that it is possible to measure the level of moisture on specific parts of the lining and that the comparison of surveys performed at different times can allow asset managers to react before a seepage is established, potentially reducing the risk of system disruption caused by water ingress in tunnels. The data also revealed that this technique could be used for other purposes, such as examining the condition of other assets such as brackets, cable supports and broken light bulbs. The third case study was performed using a Terrestrial Laser Scanner, Close-Range Photogrammetry and Total Station Survey to identify defects in structures. In order to test these technologies, a wing wall, located on the north-east wing of the HC3 underbridge at Ladbroke Grove Station, was chosen. This case study demonstrated that LUL can easily implement this type of technology to inspect rapidly their buildings and structures, being able to identify defects and monitor their assets for translation, rotation and changes in shape during changes in loading or the decay of the structure (insidious decline) and the construction of nearby assets. In this research, a large volume of data was captured, and further work is needed in order to manage the data using ‘big data’ concepts. Although it may not be possible to fully understand the insidious decline of an asset, the use of these techniques allows us to better understand how a civil asset behaves, potentially reducing the amount of reactive maintenance to a minimum, consequently reducing service costs and falls in revenue due to disruptions in the system. To successfully analyse the data from new technologies a combination of skills is required and different or retrained personal will be needed

Defect detection in infrared thermography by deep learning algorithms

L'évaluation non destructive (END) est un domaine permettant d'identifier tous les types de dommages structurels dans un objet d'intérêt sans appliquer de dommages et de modifications permanents. Ce domaine fait l'objet de recherches intensives depuis de nombreuses années. La thermographie infrarouge (IR) est l'une des technologies d'évaluation non destructive qui permet d'inspecter, de caractériser et d'analyser les défauts sur la base d'images infrarouges (séquences) provenant de l'enregistrement de l'émission et de la réflexion de la lumière infrarouge afin d'évaluer les objets non autochauffants pour le contrôle de la qualité et l'assurance de la sécurité. Ces dernières années, le domaine de l'apprentissage profond de l'intelligence artificielle a fait des progrès remarquables dans les applications de traitement d'images. Ce domaine a montré sa capacité à surmonter la plupart des inconvénients des autres approches existantes auparavant dans un grand nombre d'applications. Cependant, en raison de l'insuffisance des données d'entraînement, les algorithmes d'apprentissage profond restent encore inexplorés, et seules quelques publications font état de leur application à l'évaluation non destructive de la thermographie (TNDE). Les algorithmes d'apprentissage profond intelligents et hautement automatisés pourraient être couplés à la thermographie infrarouge pour identifier les défauts (dommages) dans les composites, l'acier, etc. avec une confiance et une précision élevée. Parmi les sujets du domaine de recherche TNDE, les techniques d'apprentissage automatique supervisées et non supervisées sont les tâches les plus innovantes et les plus difficiles pour l'analyse de la détection des défauts. Dans ce projet, nous construisons des cadres intégrés pour le traitement des données brutes de la thermographie infrarouge à l'aide d'algorithmes d'apprentissage profond et les points forts des méthodologies proposées sont les suivants: 1. Identification et segmentation automatique des défauts par des algorithmes d'apprentissage profond en thermographie infrarouge. Les réseaux neuronaux convolutifs (CNN) pré-entraînés sont introduits pour capturer les caractéristiques des défauts dans les images thermiques infrarouges afin de mettre en œuvre des modèles basés sur les CNN pour la détection des défauts structurels dans les échantillons composés de matériaux composites (diagnostic des défauts). Plusieurs alternatives de CNNs profonds pour la détection de défauts dans la thermographie infrarouge. Les comparaisons de performance de la détection et de la segmentation automatique des défauts dans la thermographie infrarouge en utilisant différentes méthodes de détection par apprentissage profond : (i) segmentation d'instance (Center-mask ; Mask-RCNN) ; (ii) détection d’objet (Yolo-v3 ; Faster-RCNN) ; (iii) segmentation sémantique (Unet ; Res-unet); 2. Technique d'augmentation des données par la génération de données synthétiques pour réduire le coût des dépenses élevées associées à la collecte de données infrarouges originales dans les composites (composants d'aéronefs.) afin d'enrichir les données de formation pour l'apprentissage des caractéristiques dans TNDE; 3. Le réseau antagoniste génératif (GAN convolutif profond et GAN de Wasserstein) est introduit dans la thermographie infrarouge associée à la thermographie partielle des moindres carrés (PLST) (réseau PLS-GANs) pour l'extraction des caractéristiques visibles des défauts et l'amélioration de la visibilité des défauts pour éliminer le bruit dans la thermographie pulsée; 4. Estimation automatique de la profondeur des défauts (question de la caractérisation) à partir de données infrarouges simulées en utilisant un réseau neuronal récurrent simplifié : Gate Recurrent Unit (GRU) à travers l'apprentissage supervisé par régression.Non-destructive evaluation (NDE) is a field to identify all types of structural damage in an object of interest without applying any permanent damage and modification. This field has been intensively investigated for many years. The infrared thermography (IR) is one of NDE technology through inspecting, characterize and analyzing defects based on the infrared images (sequences) from the recordation of infrared light emission and reflection to evaluate non-self-heating objects for quality control and safety assurance. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. This field has shown its ability to overcome most of the disadvantages in other approaches existing previously in a great number of applications. Whereas due to the insufficient training data, deep learning algorithms still remain unexplored, and only few publications involving the application of it for thermography nondestructive evaluation (TNDE). The intelligent and highly automated deep learning algorithms could be coupled with infrared thermography to identify the defect (damages) in composites, steel, etc. with high confidence and accuracy. Among the topics in the TNDE research field, the supervised and unsupervised machine learning techniques both are the most innovative and challenging tasks for defect detection analysis. In this project, we construct integrated frameworks for processing raw data from infrared thermography using deep learning algorithms and highlight of the methodologies proposed include the following: 1. Automatic defect identification and segmentation by deep learning algorithms in infrared thermography. The pre-trained convolutional neural networks (CNNs) are introduced to capture defect feature in infrared thermal images to implement CNNs based models for the detection of structural defects in samples made of composite materials (fault diagnosis). Several alternatives of deep CNNs for the detection of defects in the Infrared thermography. The comparisons of performance of the automatic defect detection and segmentation in infrared thermography using different deep learning detection methods: (i) instance segmentation (Center-mask; Mask-RCNN); (ii) objective location (Yolo-v3; Faster-RCNN); (iii) semantic segmentation (Unet; Res-unet); 2. Data augmentation technique through synthetic data generation to reduce the cost of high expense associated with the collection of original infrared data in the composites (aircraft components.) to enrich training data for feature learning in TNDE; 3. The generative adversarial network (Deep convolutional GAN and Wasserstein GAN) is introduced to the infrared thermography associated with partial least square thermography (PLST) (PLS-GANs network) for visible feature extraction of defects and enhancement of the visibility of defects to remove noise in Pulsed thermography; 4. Automatic defect depth estimation (Characterization issue) from simulated infrared data using a simplified recurrent neural network: Gate Recurrent Unit (GRU) through the regression supervised learning

Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing

A methodology based on step-heating thermography for predicting the length dimension of small defects in additive manufacturing from temperature data measured on thermal images is proposed. Regression learners were applied with different configurations to predict the length of the defects. These algorithms were trained using large datasets generated with Finite Element Method simulations. The different predictive methods obtained were optimized using Bayesian inference. Using predictive methods generated and based on intrinsic performance results, knowing the material characteristics, the defect length can be predicted from single temperature data in defect and non-defect zone. Thus, the developed algorithms were implemented in a laboratory set-up carried out on ad-hoc manufactured parts of Nylon and polylactic acid which include induced defects with different sizes and thicknesses. Using the trained algorithm, the deviation of the predicted results for the defect size varied between 13% and 37% for PLA and between 13% and 36% for Nylon.This research has been funded by Ministry of Science and Innovation (Government of Spain) through the research project titled Fusion of nondestructive technologies and numerical simulation methods for the inspection and monitoring of joints in new materials and additive manufacturing processes (FaTIMA) with code RTI2018-099850-B-I00

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

Eddy current pulsed thermography for non-destructive evaluation of carbon fibre reinforced plastic for wind turbine blades

PhD ThesisThe use of Renewable energy such as wind power has grown rapidly over the past ten years. However, the poor reliability and high lifecycle costs of wind energy can limit power generation. Wind turbine blades suffer from relatively high failure rates resulting in long downtimes. The motivation of this research is to improve the reliability of wind turbine blades via non-destructive evaluation (NDE) for the early warning of faults and condition-based maintenance. Failure in wind turbine blades can be categorised as three types of major defect in carbon fibre reinforced plastic (CFRP), which are cracks, delaminations and impact damages. To detect and characterise those defects in their early stages, this thesis proposes eddy current pulsed thermography (ECPT) NDE method for CFRP-based wind turbine blades. The ECPT system is a redesigned extension of previous work. Directional excitation is applied to overcome the problems of non-homogeneous and anisotropic properties of composites in both numerical and experimental studies. Through the investigation of the multiple-physical phenomena of electromagnetic-thermal interaction, defects can be detected, classified and characterised via numerical simulation and experimental studies. An integrative multiple-physical ECPT system can provide transient thermal responses under eddy current heating inside a sample. It is applied for the measurement and characterisation of different samples. Samples with surface defects such as cracks are detected from hot-spots in thermal images, whereas internal defects, like delamination and impact damage, are detected through thermal or heat flow patterns. For quantitative NDE, defect detection, characterisation and classification are carried out at different levels to deal with various defect locations and fibre textures. Different approaches for different applications are tested and compared via samples with crack, delamination and impact damage. Comprehensive transient feature extraction at the three different levels of the pixel, local area and pattern are developed and implemented with respect to defect location in terms of the thickness and complexity of fibre texture. Three types of defects are detected and classified at those three levels. The transient responses at pixel level, flow patterns at local area level, and principal or independent components at pattern level are derived for defect classification. Features at the pixel and local area levels are extracted in order to gain quantitative information about the defects. Through comparison of the performance of evaluations at those three levels, the pixel level is shown to be good at evaluating surface defects, in particular within uni- directional fibres. Meanwhile the local area level has advantages for detecting deeper defects such as delamination and impact damage, and in specimens with multiple fibre orientations, the pattern level is useful for the separation of defective patterns and fibre texture, as well as in distinguishing multiple defects.Engineering and Physical Sciences Research Council(EPSRC), Frame Programme 7(FP7